Navigating large-scale online discussions is difficult due to their rapid pace and high volume of content. Platforms like Reddit employ “threads’’ to visually organize parallel discussions, but deep nesting obscures conversation flow. For moderators, this fragmentation compounds the difficulty of following evolving conversations and maintaining context across threads, which limits timely and effective moderation. In this paper, we present Needle, an interactive system that applies visual analytics to summarize key conversational metrics: activity, toxicity, and voting trends over time. Needle provides both high-level overviews and detailed breakdowns of threads, enabling moderators to identify priority areas without reading through entire nested conversations. Through a user study with ten Reddit moderators, we find that Needle provides a practical solution to maintain contextual understanding when navigating threaded discussions. Based on these findings, we propose design guidelines for future visualization-based tools that shape how people consume, interpret, and make sense of large-scale online discussions.

Research into community content moderation often assumes that moderation teams govern with a single, unified voice. However, recent work has found that moderators disagree with one another at modest, but concerning rates. The problem is not the root disagreements themselves. Subjectivity in moderation is unavoidable, and there are clear benefits to including diverse perspectives within a moderation team. Instead, the crux of the issue is that, due to resource constraints, moderation decisions end up being made by individual decision-makers. The result is decision-making that is inconsistent, which is frustrating for community members. To address this, we develop Venire, an ML-backed system for panel review on Reddit. Venire uses a machine learning model trained on log data to identify the cases where moderators are most likely to disagree. Venire fast-tracks these cases for multi-person review. Ideally, Venire allows moderators to surface and resolve disagreements that would have otherwise gone unnoticed. We conduct three studies through which we design and evaluate Venire: a set of formative interviews with moderators, technical evaluations on two datasets, and a think-aloud study in which moderators used Venire to make decisions on real moderation cases. Quantitatively, we demonstrate that Venire is able to improve decision consistency and surface latent disagreements. Qualitatively, we find that Venire helps moderators resolve difficult moderation cases more confidently. Venire represents a novel paradigm for human-AI content moderation, and shifts the conversation from replacing human decision-making to supporting it.

The sudden popularity communities gain via algorithmically-curated "trending'" or "hot" social media feeds can be beneficial or disruptive. On one hand, increased attention often brings new users and promotes community growth. On the other hand, the unexpected influx of newcomers can burden already overworked moderation teams. To examine the impact of sudden popularity, we studied 6,306 posts that reached Reddit's front page---a feed called r/popular that millions of users browse daily---and the effects of sudden popularity within 1,320 subreddits. We find that on average, r/popular posts have 45 times the comments, 42 times the removed comments, and 70 times the number of newcomers compared to posts from the same community that did not reach r/popular. Additionally, r/popular posts led to a peak 85% median increase in the subreddit's comment rate, and these effects lingered for about 12 hours. Our regression analysis shows that stricter moderation and previous r/popular appearances were associated with shorter and less intense effects on the community. By quantifying the differential effects of sudden popularity, we provide recommendations for moderators to promote stability and community resilience in the face of unexpected disruptions.

The role of a moderator is often characterized as solely punitive, however, moderators have the power to not only execute reactive and punitive actions but also create norms and support the values they want to see within their communities. One way moderators can proactively foster healthy communities is through positive reinforcement, but we do not currently know whether moderators on Reddit enforce their norms by providing positive feedback to desired contributions. To fill this gap in our knowledge, we surveyed 115 Reddit moderators to build two taxonomies: one for the content and behavior that actual moderators want to encourage and another taxonomy of actions moderators take to encourage desirable contributions. We found that prosocial behavior, engaging with other users, and staying within the topic and norms of the subreddit are the most frequent behaviors that moderators want to encourage. We also found that moderators are taking actions to encourage desirable contributions, specifically through built-in Reddit mechanisms (e.g., upvoting), replying to the contribution, and explicitly approving the contribution in the moderation queue. Furthermore, moderators reported taking these actions specifically to reinforce desirable behavior to the original poster and other community members, even though many of the actions are anonymous, so the recipients are unaware that they are receiving feedback from moderators. Importantly, some moderators who do not currently provide feedback do not object to the practice. Instead, they are discouraged by the lack of explicit tools for positive reinforcement and the fact that their fellow moderators are not currently engaging in methods for encouragement. We consider the taxonomy of actions moderators take, the reasons moderators are deterred from providing encouragement, and suggestions from the moderators themselves to discuss implications for designing tools to provide positive feedback.

Moderators are at the core of maintaining healthy online communities. For these moderators, who are often volunteers from the community, filtering through content and responding to misbehavior on time has become increasingly challenging as online communities continue to grow. To address such challenges of scale, recent research has looked into designing better tools for moderators of various platforms (e.g. Reddit, Twitch, Facebook, and Twitter). In this paper, we focus on Discord, a platform where communities are typically involved in large, synchronous group chats, creating an environment with a faster pace and a lack of structure compared to previously studied platforms. To tackle the unique challenges presented by Discord, we developed a new human-AI system called ConvEx for exploring online conversations. ConvEx is an AI-augmented version of the standard Discord interface designed to help moderators be proactive in identifying and preventing potential problems. It provides visual embeddings of conversational metrics, such as activity and toxicity levels, and can be extended to visualize other metrics. Through a user study with eight active moderators of Discord servers, we found that ConvEx supported several high-level strategies in monitoring a server and analyzing conversations. ConvEx allowed moderators to obtain a holistic view of activity across multiple channels on the server while guiding their attention towards problematic conversations and messages in a channel, helping them identify important contextual information to obtain reliable information from the AI analysis while also being able to pick up on contextual nuances which the AI missed. We conclude with design considerations for integrating AI into future interfaces for moderating synchronous, unstructured online conversations.

Contemporary mobile devices with touchscreens capture the X/Y position of finger tips on the screen and pass these coordinates to applications as though the input were points in space. Of course, human hands are much more sophisticated, able to form rich 3D poses capable of far more complex interactions than poking at a screen. In this paper, we describe how conventional capacitive touchscreens can be used to estimate 3D hand pose, enabling richer interaction opportunities. Importantly, our software-only approach requires no special or new sensors, either internal or external. As a proof of concept, we use an off-the-shelf Samsung Tablet flashed with a custom kernel. After describing our software pipeline, we report findings from our user study, we conclude with several example applications we built to illustrate the potential of our approach.

The growth of digital information and the richness of data shared online make it increasingly valuable to be able to process large amounts of data at a very high throughput rate. At the same time, rising interest in natural language processing (NLP) has resulted in the development of a great number of algorithms designed to perform a variety of NLP tasks. There is a need for frameworks that enable multiple users and applications to run individual or a combination of NLP algorithms to derive relevant information from data [1]. In this work, we take multiple NLP algorithms that adhere to the ADEPT framework and deploy them on distributed processing architectures to satisfy the dual needs of serving a large user group and meeting high throughput standards, while reducing the time from lab to production environment. The ADEPT framework provides a set of uniform APIs for interacting with a diverse set of NLP algorithms by defining a set of data structures for representing NLP concepts [2]. It offers multiple access points for interacting with these algorithms; a REST API, a serialized Data API, and processor components that can be used in a larger pipeline. The comprehensive ADEPT architecture can support algorithms that perform sentence-level, document-level, or corpus-level text processing, allowing a wide range of NLP algorithms to make use of the framework. ADEPT interfaces allow parallelization to occur at an optimum level for each algorithm. Amazon Web Services (AWS) consists of a stack of technologies commonly used in the commercial sphere to host web applications designed to scale rapidly with a growing user base. The Amazon Elastic Compute Cloud (EC2) and its auto-scaling feature in particular provide a means of reliably and efficiently scaling a service to meet traffic demands. Hadoop and Spark are top level Apache projects designed to enable massive parallelization of data processing. Hadoop employs the MapReduce programming model and uses a distributed file system to store data. It is a widely used processing framework with proven potential for very high throughput. Spark is a distributed processing framework that makes use of in-memory primitives, enabling a significant performance advantage over Hadoop in certain uses at the cost of higher memory requirements [3]. Spark processes are able but not required to fit into the MapReduce model, allowing algorithms to be adapted for use in a Spark context with minimal effort. To handle high volume of concurrent requests of DEFT algorithms, we created a mechanism to deploy the algorithms on an AWS stack [4]. We extended the ADEPT framework to create installers to deploy algorithms on an AWS EC2 node. We preserve the EC2 instance along with the algorithm and grow or shrink the number of instances to accommodate the request volume. In summary, we demonstrate that NLP algorithms can be rapidly scaled by leveraging the ADEPT framework, parallelization models, and virtualization technologies to meet the growing demands of high volume and throughput. The ADEPT framework allows managing a diverse set of NLP algorithms. We adapt the NLP algorithms to fit into Hadoop and Spark architectures in an effort to maximize their throughput. We explore the viability of using Amazon EC2 to meet and rapidly scale based on usage demands.